






"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE   :							Optional[int]          =		logging.get_logger(__name__)







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[str]						,							_lowerCamelCase			:						str=False					) ->							Dict:
  lowerCamelCase_       	=		[]

  # fmt: off
  # stem:
  rename_keys.append(('cls_token', 'vit.embeddings.cls_token')					)
  rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')					)

  rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')					)
  rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')					)

  # backbone
  rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')					)
  rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')					)
  rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')					)

  for stage_idx in range(len(config.backbone_config.depths					)					):
    for layer_idx in range(config.backbone_config.depths[stage_idx]					):
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''')					)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''')					)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''')					)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''')					)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''')					)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''')					)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''')					)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''')					)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''')					)

    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''')					)
    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''')					)
    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''')					)

  # transformer encoder
  for i in range(config.num_hidden_layers					):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')					)
    rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')					)
    rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')					)
    rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')					)
    rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')					)
    rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')					)
    rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')					)
    rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')					)
    rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')					)
    rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')					)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('norm.weight', 'layernorm.weight'),
            ('norm.bias', 'layernorm.bias'),
            ('pre_logits.fc.weight', 'pooler.dense.weight'),
            ('pre_logits.fc.bias', 'pooler.dense.bias'),
        ]					)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    lowerCamelCase_       	=		[(pair[0], pair[1][4:]) if pair[1].startswith('vit'					) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('norm.weight', 'vit.layernorm.weight'),
            ('norm.bias', 'vit.layernorm.bias'),
            ('head.weight', 'classifier.weight'),
            ('head.bias', 'classifier.bias'),
        ]					)
  # fmt: on

  return rename_keys







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						str						,							_lowerCamelCase			:						str=False					) ->							Optional[int]:
  for i in range(config.num_hidden_layers					):
    if base_model:
      lowerCamelCase_       	=		''
    else:
      lowerCamelCase_       	=		'vit.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    lowerCamelCase_       	=		state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''					)
    lowerCamelCase_       	=		state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''					)
    # next, add query, keys and values (in that order) to the state dict
    lowerCamelCase_       	=		in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCamelCase_       	=		in_proj_bias[: config.hidden_size]
    lowerCamelCase_       	=		in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase_       	=		in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCamelCase_       	=		in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase_       	=		in_proj_bias[-config.hidden_size :]







def 	lowerCamelCase__			(				_lowerCamelCase			:						Union[str, Any]					) ->							List[str]:
  lowerCamelCase_       	=		['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_lowerCamelCase						,							_lowerCamelCase					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict						,							_lowerCamelCase			:						Optional[int]						,							_lowerCamelCase			:						Optional[int]					) ->							Union[str, Any]:
  lowerCamelCase_       	=		dct.pop(_lowerCamelCase					)
  lowerCamelCase_       	=		val







def 	lowerCamelCase__			(				) ->							List[Any]:
  lowerCamelCase_       	=		'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCamelCase_       	=		Image.open(requests.get(_lowerCamelCase						,							stream=_lowerCamelCase					).raw					)
  return im







@torch.no_grad()
def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						Optional[int]						,							_lowerCamelCase			:						Optional[Any]=False					) ->							List[Any]:
  lowerCamelCase_       	=		BitConfig(
      global_padding='same'						,							layer_type='bottleneck'						,							depths=(3, 4, 9)						,							out_features=['stage3']						,							embedding_dynamic_padding=_lowerCamelCase						,							)
  lowerCamelCase_       	=		ViTHybridConfig(backbone_config=_lowerCamelCase						,							image_size=384						,							num_labels=1000					)
  lowerCamelCase_       	=		False

  # load original model from timm
  lowerCamelCase_       	=		timm.create_model(_lowerCamelCase						,							pretrained=_lowerCamelCase					)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  lowerCamelCase_       	=		timm_model.state_dict()
  if base_model:
    remove_classification_head_(_lowerCamelCase					)
  lowerCamelCase_       	=		create_rename_keys(_lowerCamelCase						,							_lowerCamelCase					)
  for src, dest in rename_keys:
    rename_key(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)
  read_in_q_k_v(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)

  lowerCamelCase_       	=		'huggingface/label-files'
  lowerCamelCase_       	=		'imagenet-1k-id2label.json'
  lowerCamelCase_       	=		json.load(open(hf_hub_download(_lowerCamelCase						,							_lowerCamelCase						,							repo_type='dataset'					)						,							'r'					)					)
  lowerCamelCase_       	=		{int(_lowerCamelCase					): v for k, v in idalabel.items()}
  lowerCamelCase_       	=		idalabel
  lowerCamelCase_       	=		{v: k for k, v in idalabel.items()}

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCamelCase_       	=		ViTHybridModel(_lowerCamelCase					).eval()
  else:
    lowerCamelCase_       	=		ViTHybridForImageClassification(_lowerCamelCase					).eval()
  model.load_state_dict(_lowerCamelCase					)

  # create image processor
  lowerCamelCase_       	=		create_transform(**resolve_data_config({}						,							model=_lowerCamelCase					)					)
  lowerCamelCase_       	=		transform.transforms

  lowerCamelCase_       	=		{
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

  lowerCamelCase_       	=		ViTHybridImageProcessor(
      do_resize=_lowerCamelCase						,							size={'shortest_edge': timm_transforms[0].size}						,							resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,							do_center_crop=_lowerCamelCase						,							crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}						,							do_normalize=_lowerCamelCase						,							image_mean=timm_transforms[-1].mean.tolist()						,							image_std=timm_transforms[-1].std.tolist()						,							)

  lowerCamelCase_       	=		prepare_img()
  lowerCamelCase_       	=		transform(_lowerCamelCase					).unsqueeze(0					)
  lowerCamelCase_       	=		processor(_lowerCamelCase						,							return_tensors='pt'					).pixel_values

  # verify pixel values
  assert torch.allclose(_lowerCamelCase						,							_lowerCamelCase					)

  # verify logits
  with torch.no_grad():
    lowerCamelCase_       	=		model(_lowerCamelCase					)
    lowerCamelCase_       	=		outputs.logits

  print('Predicted class:'						,							logits.argmax(-1					).item()					)
  if base_model:
    lowerCamelCase_       	=		timm_model.forward_features(_lowerCamelCase					)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(_lowerCamelCase						,							outputs.pooler_output						,							atol=1e-3					)
  else:
    lowerCamelCase_       	=		timm_model(_lowerCamelCase					)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(_lowerCamelCase						,							outputs.logits						,							atol=1e-3					)
  print('Looks ok!'					)

  if pytorch_dump_folder_path is not None:
    Path(_lowerCamelCase					).mkdir(exist_ok=_lowerCamelCase					)
    print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''					)
    model.save_pretrained(_lowerCamelCase					)
    print(F'''Saving processor to {pytorch_dump_folder_path}'''					)
    processor.save_pretrained(_lowerCamelCase					)

  if push_to_hub:
    print(F'''Pushing model and processor to the hub {vit_name}'''					)
    model.push_to_hub(F'''ybelkada/{vit_name}'''					)
    processor.push_to_hub(F'''ybelkada/{vit_name}'''					)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							str          =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--vit_name''',
      default='''vit_base_r50_s16_384''',
      type=str,
      help='''Name of the hybrid ViT timm model you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
  )
  parser.add_argument(
      '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
  )

  _SCREAMING_SNAKE_CASE   :							List[str]          =		parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizerFast
    SCREAMING_SNAKE_CASE		:	int		=					True






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      lowerCamelCase_       	=		DistilBertTokenizer.from_pretrained('distilbert-base-uncased'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]




